Objective

To determine if functional measures of ambulation can be accurately classified using clinical measures; demographics; personal, psychosocial, and environmental factors; and limb accelerations (LAs) obtained during sleep among individuals with chronic, motor incomplete spinal cord injury (SCI) in an effort to guide future, longitudinal predictions models.

Design

Cross-sectional, 1-5 days of data collection.

Setting

Community-based data collection.

Participants

Adults with chronic (>1 year), motor incomplete SCI (N=27).

Interventions

Not applicable.

Main Outcome Measures

Ambulatory ability based on the 10-m walk test (10MWT) or 6-minute walk test (6MWT) categorized as nonambulatory, household ambulator (0.01-0.44 m/s, 1-204 m), or community ambulator (>0.44 m/s, >204 m). A random forest model classified ambulatory ability using input features including clinical measures of strength, sensation, and spasticity; demographics; personal, psychosocial, and environmental factors including pain, environmental factors, health, social support, self-efficacy, resilience, and sleep quality; and LAs measured during sleep. Machine learning methods were used explicitly to avoid overfitting and minimize the possibility of biased results.

Results

The combination of LA, clinical, and demographic features resulted in the highest classification accuracies for both functional ambulation outcomes (10MWT=70.4%, 6MWT=81.5%). Adding LAs, personal, psychosocial, and environmental factors, or both increased the accuracy of classification compared with the clinical/demographic features alone. Clinical measures of strength and sensation (especially knee flexion strength), LA measures of movement smoothness, and presence of pain and comorbidities were among the most important features selected for the models.

Conclusions

The addition of LA and personal, psychosocial, and environmental features increased functional ambulation classification accuracy in a population with incomplete SCI for whom improved prognosis for mobility outcomes is needed. These findings provide support for future longitudinal studies that use LA; personal, psychosocial, and environmental factors; and advanced analyses to improve clinical prediction rules for functional mobility outcomes.

中文翻译：

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通过纳入睡眠期间的肢体加速度和个人因素来改善脊髓损伤后功能性步行的预测

客观的

确定是否可以使用临床措施对行走的功能措施进行准确分类；人口统计资料；个人、社会心理和环境因素；和肢体加速度 (LAs) 在患有慢性、运动性不完全性脊髓损伤 (SCI) 的个体睡眠期间获得，以努力指导未来的纵向预测模型。

设计

横截面，1-5 天的数据收集。

环境

基于社区的数据收集。

参加者

患有慢性（> 1 年）、运动不完全性脊髓损伤的成人 (N=27)。

干预措施

不适用。

主要观察指标

基于 10 米步行测试 (10MWT) 或 6 分钟步行测试 (6MWT) 的步行能力分为非步行、家庭步行（0.01-0.44 m/s、1-204 m）或社区步行（>0.44 m/s）秒，>204 米）。随机森林模型使用输入特征对行走能力进行分类，包括力量、感觉和痉挛的临床测量；人口统计资料；个人、社会心理和环境因素，包括疼痛、环境因素、健康、社会支持、自我效能、适应力和睡眠质量；和在睡眠期间测量的 LA。明确使用机器学习方法来避免过度拟合并最大限度地减少结果偏差的可能性。

结果

LA、临床和人口统计特征的组合导致两种功能性步行结果的最高分类准确度（10MWT=70.4%，6MWT=81.5%）。与单独的临床/人口统计特征相比，添加 LA、个人、社会心理和环境因素，或两者都增加了分类的准确性。力量和感觉（尤其是膝关节屈曲力量）的临床测量、运动平滑度的 LA 测量以及疼痛和合并症的存在是为模型选择的最重要的特征。

结论

增加 LA 和个人、社会心理和环境特征可提高不完全 SCI 人群的功能性步行分类准确性，需要改善流动性结果的预后。这些发现为未来使用 LA 的纵向研究提供了支持；个人、社会心理和环境因素；和高级分析，以改进功能活动结果的临床预测规则。